Abstract

There is an increasing demand on high-quality software as software bugs have an economic impact not only on software projects, but also on national economies in general. Software quality is achieved via the main quality assurance activities of testing and code reviewing. However, these activities are expensive, thus they need to be carried out efficiently. Auxiliary software quality tools such as bug detection and bug prediction tools help developers focus their testing and reviewing activities on the parts of software that more likely contain bugs. However, these tools are far from adoption as mainstream development tools. Previous research points to their inability to adapt to the peculiarities of projects and their high rate of false positives as the main obstacles of their adoption. We propose empirically-grounded analysis to improve the adaptability and efficiency of bug detection and prediction tools. For a bug detector to be efficient, it needs to detect bugs that are conspicuous, frequent, and specific to a software project. We empirically show that the null-related bugs fulfill these criteria and are worth building detectors for. We analyze the null dereferencing problem and find that its root cause lie in methods that return null. We propose an empirical solution this problem that depends on the wisdom of the crowd. For each API method, we extract the nullability measure that expresses how often the return value of this method is checked against null in the ecosystem of the API. We use nullability to annotate API methods with nullness annotation and warn developers about missing and excessive null checks. For a bug predictor to be efficient, it needs to be optimized as both a machine learning model and a software quality tool. We empirically show how feature selection and hyperparameter optimizations improve prediction accuracy. Then we optimize bug prediction to locate the maximum number of bugs in the minimum amount of code by finding the most cost-effective combination of bug prediction configurations, i.e. dependent variables, machine learning model, and response variable. We show that using both source code and change metrics as dependent variables, applying feature selection on them, then using an optimized Random Forest to predict the number of bugs results in the most cost-effective bug predictor. Throughout this thesis, we show how empirically-grounded analysis helps us achieve efficient bug prediction and detection tools and adapt them to the characteristics of each software project.

Abstract

Abundant studies have shown that visualization is advantageous for software developers, yet adopting visualization during software development is not a common practice due to the large effort involved in finding an appropriate visualization. Developers require support to facilitate that task. Among 368 papers in SOFTVIS/VISSOFT venues, we identify 86 design study papers about the application of visualization to relieve concerns in software development. We extract from these studies the task, need, audience, data source, representation, medium and tool; and we characterize them according to the subject, process and problem domain. On the one hand, we support software developers to put visualization in action by mapping existing visualization techniques to particular needs from different perspectives. On the other hand, we highlight the problem domains that are overlooked in the field and need more support.

Abstract

There is an increasing demand on high-quality software as software bugs have an economic impact not only on software projects, but also on national economies in general. Software quality is achieved via the main quality assurance activities of testing and code reviewing. However, these activities are expensive, thus they need to be carried out efficiently. Auxiliary software quality tools such as bug detection and bug prediction tools help developers focus their testing and reviewing activities on the parts of software that more likely contain bugs. However, these tools are far from adoption as mainstream development tools. Previous research points to their inability to adapt to the peculiarities of projects and their high rate of false positives as the main obstacles of their adoption. We propose empirically-grounded analysis to improve the adaptability and efficiency of bug detection and prediction tools. For a bug detector to be efficient, it needs to detect bugs that are conspicuous, frequent, and specific to a software project. We empirically show that the null-related bugs fulfill these criteria and are worth building detectors for. We analyze the null dereferencing problem and find that its root cause lie in methods that return null. We propose an empirical solution this problem that depends on the wisdom of the crowd. For each API method, we extract the nullability measure that expresses how often the return value of this method is checked against null in the ecosystem of the API. We use nullability to annotate API methods with nullness annotation and warn developers about missing and excessive null checks. For a bug predictor to be efficient, it needs to be optimized as both a machine learning model and a software quality tool. We empirically show how feature selection and hyperparameter optimizations improve prediction accuracy. Then we optimize bug prediction to locate the maximum number of bugs in the minimum amount of code by finding the most cost-effective combination of bug prediction configurations, i.e. dependent variables, machine learning model, and response variable. We show that using both source code and change metrics as dependent variables, applying feature selection on them, then using an optimized Random Forest to predict the number of bugs results in the most cost-effective bug predictor. Throughout this thesis, we show how empirically-grounded analysis helps us achieve efficient bug prediction and detection tools and adapt them to the characteristics of each software project.

Abstract

Concurrency issues are inherently harder to identify and fix than issues in sequential programs, due to aspects like indeterminate order of access to shared resources and thread synchronisation. Live debuggers are often used by developers to gain insights into the behaviour of concurrent programs by exploring the call stacks of threads. Nevertheless, contemporary live debuggers for concurrent programs are usually sequential debuggers augmented with the ability to display different threads in isolation. To these debuggers every thread call stack begins with a designated start routine and the calls that led to the creation of the thread are not visible, as they are part of a different thread. This requires developers to manually link stack traces belonging to related but distinct threads, adding another burden to the already difficult act of debugging concurrent programs. To improve debugging of concurrent programs we address the problem of incomplete call stacks in debuggers through a thread and debugger model that enables live debugging of child threads within the context of their parent threads. The proposed debugger operates on a virtual thread that merges together multiple relevant threads. To better understand the features of debuggers for concurrent programs we present an in-depth discussion of the concurrency related features in current live debuggers. We test the applicability of the proposed model by instantiating it for simple threads, local and remote promises, and a remote object-oriented database. Starting from these use cases we further discuss implementation details ensuring a practical approach.

Abstract

Today, no existing club management software offers clubs a possibility to organize the human resources at their events in an adequate way. Available applications do not provide organizing committees with enough functionality to ensure that tasks and responsibilities are distributed fairly between a club’s members over the course of a full season. Instead, clubs often rely on techniques such as e-mail and spreadsheets to keep track of the tasks at an event or the member collaboration throughout a season. The use of these simple techniques and processes increases the complexity for the responsible event managers. Their work needs more communication, takes more time, and is more error prone. On the other hand, club members need to keep in mind when and where they have to help at upcoming events as they do not have a way to look up this information except for contacting the responsible person directly. In this thesis we describe the web application ClubAdmin which has been developed for the floorball club Unihockeyteam Eggiwil1. Aside from member, sponsor, and open issue management modules, our system provides an event management module which is accessible to the board (as the event organizer) and to the club members. The club can manage all aspects of event management such that existing cumbersome workflows are fully replaced.